Restaurant delivery is a multi-platform reality. Most operators running third-party delivery aren't choosing between DoorDash and UberEats; they're running both. Plus GrubHub. And maybe their own online ordering channel on top of that.
Each platform comes with its own operator dashboard. Each generates its own reports. Each uses its own terminology for what are often the same underlying metrics. And none of them, by design, shows you how they stack up against each other or against your in-store operation.
So understanding your delivery business with the same clarity you have about in-house sales takes either significant manual effort or a data consolidation layer that most operators simply haven't built yet.
Why Platform-Native Dashboards Aren't Enough
DoorDash's operator portal shows you DoorDash performance. The UberEats portal shows you UberEats performance. These tools work well for managing each platform individually: adjusting hours, updating menus, responding to reviews. What they aren't designed for is cross-platform analysis, and they don't connect to your POS data or your internal financial reporting.
As a result, the questions that matter most to a delivery-focused operation often can't be answered from any single platform. Which delivery channel generates the most profitable orders after commission fees? How does delivery average ticket compare to in-house average ticket by location? Are delivery orders cannibalizing in-house traffic or adding incremental revenue? Which platform carries the highest refund rate, and is it concentrated in specific menu items or specific locations?
These are operational and strategic questions. Answering them requires data that lives across multiple systems and needs to be brought together in one place.
Building a Unified Delivery Reporting Layer
A unified delivery reporting layer typically involves three components: data collection (connecting to each platform's API to pull order-level data in real time), data normalization (mapping each platform's fields to a common schema so they're comparable), and data surfacing (building dashboards and reports that present the consolidated view).
The API connections are the most technically intensive piece. Each platform has its own API structure, authentication model, and rate limits. Once they're built and maintained, though, they provide a continuous feed of order data that populates the reporting layer automatically.
Normalization is where a lot of the analytical value gets created. Because each platform reports differently, meaningful comparisons depend on deliberate decisions about how to define and calculate each metric consistently across sources. What counts as a "completed order"? How are refunds categorized? How are commission fees allocated? Getting these definitions right is what makes cross-platform comparison genuinely useful rather than misleading.
What Consolidated Delivery Reporting Enables
Once you have a unified view of delivery performance across every platform, a set of operational capabilities opens up that weren't possible before.
Channel allocation decisions (which platforms to prioritize, where to run promotions, where to invest in commission optimization) can rest on actual comparative performance data instead of each platform's self-reported metrics. Menu engineering can account for delivery-specific item performance rather than assuming in-house popularity maps cleanly to delivery success. Guest sentiment analysis can aggregate ratings and reviews from all platforms to surface systemic issues instead of platform-specific complaints. And financial reporting can fold in delivery channel revenue and commission costs to produce an accurate picture of delivery profitability.
The data for all of this already exists. It's generated every time an order is placed on any platform. The real work is building the infrastructure to make it useful.
Suntek Solutions builds custom integrations and reporting across delivery platforms like DoorDash, UberEats, GrubHub, and OLO. Talk about your delivery data at SuntekSolutions.io/calendar.
